Battle of the Models

Compare specific LLM models, context windows, and capabilities.

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VS
No matches found

DeepSeek Coder V2

A-TIER

Ollama

Intelligence Score 85/100
Model Popularity 0 votes
Context Window 64K tokens
Pricing Model Free / Open
View Provider Analysis →

Llama 3 8B Instruct

BentoML

Intelligence Score 71/100
Context Window 8K
Pricing Model Commercial / Paid
Model Popularity 0 votes
View Provider Analysis →
FINAL VERDICT

DeepSeek Coder V2 Wins

With an intelligence score of 85/100 vs 71/100, DeepSeek Coder V2 outperforms Llama 3 8B Instruct by 14 points.

HEAD-TO-HEAD

Detailed Comparison

Feature
DeepSeek Coder V2
Llama 3 8B Instruct
Context Window
64K tokens 8K
Architecture
Dense Transformer Transformer (Open Weight)
Est. MMLU Score
~80-84% ~65-69%
Release Date
2024 2024
Pricing Model
Free Tier Paid / Commercial
Rate Limit (RPM)
Hardware limited Hardware dependent
Daily Limit
Unlimited Unlimited
Capabilities
No specific data
No specific data
Performance Tier
A-Tier (Excellent) C-Tier (Good)
Speed Estimate
Medium ⚡ Very Fast
Primary Use Case
💻 Code Generation General Purpose
Model Size
Undisclosed 8B
Limitations
  • Depends on your RAM/GPU
  • Laptop fans will spin up
  • Large models (70B+) need heavy hardware
  • Learning curve for 'Bento' concept
  • Deployment requires cloud knowledge
  • Local serving is just step 1
Key Strengths
  • Local Inference: Data never leaves your device
  • Modelfiles: Script your own system prompts
  • API: Local REST API for app integration
  • Unified Model Store
  • Distributed Runner Architecture
  • Deployment Agnostic

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